Deep neural network (DNN)-based policy models like vision-language-action (VLA) models are transformative in automating complex decision-making across applications by interpreting multi-modal data. However, scaling these models greatly increases computational costs, which presents challenges in fields like robot manipulation and autonomous driving that require quick, accurate responses. To address the need for deployment on resource-limited hardware, we propose a new quantization framework for IL-based policy models that fine-tunes parameters to enhance robustness against low-bit precision errors during training, thereby maintaining efficiency and reliability under constrained conditions. Our evaluations with representative robot manipulation for 4-bit weight-quantization on a real edge GPU demonstrate that our framework achieves up to 2.5x speedup and 2.5x energy savings while preserving accuracy. For 4-bit weight and activation quantized self-driving models, the framework achieves up to 3.7x speedup and 3.1x energy saving on a low-end GPU. These results highlight the practical potential of deploying IL-based policy models on resource-constrained devices.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
Humans are capable of learning a new behavior by observing others perform the skill. Robots can also implement this by imitation learning. Furthermore, if with external guidance, humans will master the new behavior more efficiently. So how can robots implement this? To address the issue, we present Federated Imitation Learning (FIL) in the paper. Firstly, a knowledge fusion algorithm deployed on the cloud for fusing knowledge from local robots is presented. Then, effective transfer learning methods in FIL are introduced. With FIL, a robot is capable of utilizing knowledge from other robots to …
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Technical University Darmstadt（达姆施塔特工业大学）
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed…
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulation environment due to safety, economic and time constraints. Later, the agent is applied in the real-life domain using sim-to-real methods. In this paper, we apply Imitation Learning methods that solve a robotics task in a simulated environment and use transfer learning to apply these solutions in the real-world environment. Our task is set in the Duckietown environment, where…
Everyday Robots；Robotics at Google（Google机器人学）；UC Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach the challenge from an imitation learning perspective, aiming to study how scaling and broadening the data collected can facilitate such generalization. To that end, we develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions and can be conditioned on different forms of information that convey the task, including pre-trained embeddings of natural language or…
In recent years, we have seen an emergence of data-driven approaches in robotics. However, most existing efforts and datasets are either in simulation or focus on a single task in isolation such as grasping, pushing or poking. In order to make progress and capture the space of manipulation, we would need to collect a large-scale dataset of diverse tasks such as pouring, opening bottles, stacking objects etc. But how does one collect such a dataset? In this paper, we present the largest available robotic-demonstration dataset (MIME) that contains 8260 human-robot demonstrations over 20 differen…
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI Thrust）；The Hong Kong University of Science and Technology（香港科技大学）
The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning and imitation learning are becoming research hotspots with their respective characteristics. However, the two have their own limitations in some cases, such as the high cost of data acquisition for reinforcement learning. Moreover, it is d…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）；University of Macau（澳门大学）
Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service r…
Improving Robotic Imitation Learning via Trajectory Standardization
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；D-Robotics
Imitation learning, and robot learning in general, emerged due to breakthroughs in machine learning, rather than breakthroughs in robotics. As such, evaluation metrics for robot learning are deeply rooted in those for machine learning, and focus primarily on data efficiency. We believe that a better metric for real-world robot learning is time efficiency, which better models the true cost to humans. This is a call to arms to the robot learning community to develop our own evaluation metrics, tailored towards the long-term goals of real-world robotics.
A Wearable Robotic Hand for Hand-over-Hand Imitation Learning
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Qi Zhi Institute（上海期智研究院）
University of Maryland（马里兰大学）；Syracuse University（雪城大学）
Imitation learning allows social robots to learn new skills from human teachers without substantial manual programming, but it is difficult for robotic imitation learning systems to generalize demonstrated skills as well as human learners do. Contemporary neurocomputational approaches to imitation learning achieve limited generalization at the cost of data-intensive training, and often produce opaque models that are difficult to understand and debug. In this study, we explore the viability of developing purely-neural controllers for social robots that learn to imitate by reasoning about the un…
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（日本国家先进工业科学技术研究院人工智能研究中心）；CNRS-AIST JRL (Joint Robotics Laboratory), IRL（法国国家科学研究中心与日本AIST联合机器人实验室）；Institute for AI and Robotics, Future Robotics Organization, Waseda University（早稻田大学未来机器人组织人工智能与机器人研究所）；AI Robot Association (AIRoA)（人工智能机器人协会）
Northeastern University（东北大学）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School, Northeastern University（东北大学佛山研究生院）
Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning
Cognitive Robotics, Delft University of Technology（德鲁特理工大学认知机器人学）；Bioinspired Soft Robotics Lab, Istituto Italiano di Tecnologia（意大利技术研究院仿生软机器人实验室）；Technology Innovation Institute (TII)（技术创新研究所）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电研究所）
Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-mat…
Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously explored in literature. Perhaps one of the most powerful techniques to learn complex manipulation strategies is imitation learning. However, collecting and learning from demonstrations in dexterous manipulation is quite challenging. The complex, high-dimensional action-space involved with multi-finger control often leads to poor sample efficiency of learning-based methods. In this work, we p…
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
Chair for Computer Aided Medical Procedures(CAMP), Technical University of Munich(TUM)（计算机辅助医疗程序主席职位（CAMP），慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The University of Hong Kong(HKU)（香港大学）
Osaka University（大阪大学）；National Institute of Advanced Industrial Science and Technology（国立先进工业科学技术研究院）
Robotic assembly tasks involve complex and low-clearance insertion trajectories with varying contact forces at different stages. While the nominal motion trajectory can be easily obtained from human demonstrations through kinesthetic teaching, teleoperation, simulation, among other methods, the force profile is harder to obtain especially when a real robot is unavailable. It is difficult to obtain a realistic force profile in simulation even with physics engines. Such simulated force profiles tend to be unsuitable for the actual robotic assembly due to the reality gap and uncertainty in the as…
Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, LIRIS, UMR5205（Lyon中央理工学院、CNRS、Claude Bernard Lyon 1大学、INSA Lyon、Lyon大学、LIRIS、UMR5205）；Beihang University（北航大学）；Dalian University of Technology（大连理工大学）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））
Imperial College London（伦敦帝国理工学院）；Tencent Robotics X（腾讯 Robotics X）；Bielefeld University（比勒费尔德大学）
To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreov…
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
The University of Tokyo（东京大学）；Graduate School of Information Science and Technology（信息科学与技术研究生院）；Laboratory for Intelligent Systems and Informatics（智能系统与信息学实验室）
University of Tokyo（东京大学）；Technische Universität Darmstadt（达姆施塔特工业大学）；University of Lincoln（林肯大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms develope…
University of Freiburg（弗赖堡大学）；University of Technology Nuremberg（纽伦堡工业大学）
A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this paper, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation data…
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies with high-dimensional observations as input, e.g., images. By applying GAIL to a real robot, perhaps robot policies can be obtained for daily activities like washing, folding clothes, cooking, and cleaning. However, human demonstration data are often imperfect due to mistakes, which degrade the performance of the resulting policies. We address this issue by focusing on the following features: 1) many robotic tasks are …
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compens…
Robotics at Google（谷歌机器人部门）；X, The Moonshot Factory（X（登月工厂））；UC Berkeley（加州大学伯克利分校）
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety…
